Identification of key genes and construction of microRNA–mRNA regulatory networks in multiple myeloma by integrated multiple GEO datasets using bioinformatics analysis

Identification of biomarkers in multiple myeloma: A comprehensive study combining microarray analysis and Mendelian randomization

Despite remarkable advancements in the treatment of multiple myeloma (MM), relapse remains a challenge. However, the mechanisms underlying this disease remain unclear. This study aimed to identify potential biomarkers that could open new avenues for MM treatment. Microarray data and clinical characteristics of patients with MM were obtained from the Gene Expression Omnibus database. Differential expression analysis and protein-protein interaction (PPI) network construction were used to identify hub genes associated with MM. Predictive performance was further assessed using receiver operating characteristic curves and nomogram construction. Functional enrichment analysis was conducted to investigate possible mechanisms. Mendelian randomization (MR) was used to evaluate the causal relationship between the crucial gene and MM risk. Topological analysis of the PPI network revealed five hub genes associated with MM, with myeloperoxidase (MPO) being the key gene owing to its highest degree and area under the curve values. MPO showed significant differences between patients with MM and controls across all datasets. Functional enrichment analysis revealed a strong association between MPO and immune-related pathways in MM. MR analysis confirmed a causal relationship between MPO and the risk of MM. By integrating microarray analysis and MR, we successfully identified and validated MPO as a promising biomarker for MM that is potentially implicated in MM pathogenesis and progression through immune-related pathways.

Identification of key genes and immune infiltration in multiple myeloma by bioinformatics analysis

OBJECTIVE

Multiple Myeloma (MM) is a hematologic malignant disease with unclear molecular mechanisms. This integrated bioinformatic study aimed to identify key genes, pathways and immune cell infiltration pattern in MM.

METHODS

Differentially expressed genes (DEGs) from GSE6477 and GSE16558 dataset were filtrated with R package 'limma', whose function were explored by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The key genes were selected from Protein-protein interaction network (PPI) and logistic regression model. The correlation between key genes and survival in MM was evaluated using the survival and survminer package. Additionally, immune filtration analysis was accomplished by CIBERSORT tools.

RESULTS

118 DEGs (92 up-regulated and 26 down-regulated) from two GSE datasets were identified, which were closely related with B cell receptor signaling pathway and Epstein-Barr virus infection. Furthermore, CD24 and PTPRC of five hub genes identified in PPI network were further screened out by the logistic regression model. Besides, CD24 and PTPRC expression were significantly correlated to the survival time in MM patients. Finally, MM might cause different infiltrating immune cell compositions, including increased infiltrations of B cells memory, Plasma cells, T cells CD4 memory resting, T cells follicular helper, Tregs, NK cells resting, Macrophages(M0/M1), Dendritic cells resting and Mast cells activating, and lower proportions of B cells naïve, T cells CD4 naïve, Macrophages M2 and Neutrophils.

CONCLUSION

Targeting CD24 and PTPRC as molecular markers of MM is valuable to MM therapy. Moreover, the immune cell infiltration will provide new insights into MM immunopathology.

Identification of key genes and miRNA-mRNA regulatory networks associated with bone marrow immune microenvironment regulations in multiple myeloma by integrative bioinformatics analysis

The deregulation of microRNAs (miRNAs) and genes in the bone marrow microenvironment have been involved with the pathogenesis of multiple myeloma (MM). However, the exploration of miRNA-mRNA regulatory networks in MM remains lacking. We used GSE125363, GSE125361, GSE47552, GSE2658, GSE136324, GSE16558, and GSE13591 datasets for this bioinformatics study. We identified 156 downregulated and 13 upregulated differentially expressed miRNAs (DEmiRs) in MM. The DEmiRs are associated with the enrichment of pathways mainly involved with cancers, cellular signaling, and immune regulations. We identified 112 hub genes associated with five significant clusters in MM. Moreover, we identified 9 upregulated hub genes (such as IGF1, RPS28, UBA52, CDKN1A, and CDKN2A) and 52 downregulated hub genes (such as TP53, PCNA, BRCA1, CCNB1, and MSH2) in MM that is targeted by DEmiRs. The expression of DEmiRs targeted two hub genes (CDKN2A and TP53) are correlated with the survival prognosis of MM patients. Furthermore, the expression level of CDKN2A is correlated with immune signatures, including CD4+ Regulatory T cells, T cell exhaustion, MHC Class I, immune checkpoint genes, macrophages, neutrophils, and TH2 cells in the TME of MM. Finally, we revealed the consistently deregulated expression level of key gene CDKN2A and its co-regulatory DEmiRs, including hsa-mir-192, hsa-mir-10b, hsa-mir-492, and hsa-mir-24 in the independent cohorts of MM. Identifying key genes and miRNA-mRNA regulatory networks may provide new molecular insights into the tumor immune microenvironment in MM.

Structural basis for the SUMO protease activity of the atypical ubiquitin-specific protease USPL1

Post-translational protein modifications by ubiquitin and ubiquitin-like modifiers regulate many major pathways in the cell. These modifications can be reversed by de-ubiquitinating enzymes such as ubiquitin-specific proteases (USPs). Proteolytic activity towards ubiquitin-modified substrates is common to all USP family members except for USPL1, which shows a unique preference for the ubiquitin-like modifier SUMO. Here, we present the crystal structure of USPL1 bound to SUMO2, defining the key structural elements for the unusual deSUMOylase activity of USPL1. We identify specific contacts between SUMO2 and the USPL1 subdomains, including a unique hydrogen bond network of the SUMO2 C-terminal tail. In addition, we find that USPL1 lacks major structural elements present in all canonical USPs members such as the so-called blocking loops, which facilitates SUMO binding. Our data give insight into how a structural protein scaffold designed to bind ubiquitin has evolved to bind SUMO, providing an example of divergent evolution in the USP family.

USPL1 is a non-canonical member of the ubiquitin-specific protease (USP) family with activity toward SUMO instead of ubiquitin. Here, the authors present a crystal structure of USPL1 bound to SUMO2, revealing how this enzyme has evolved to bind SUMO as an example of divergent evolution in the USP family.

Genome-wide identification of potential biomarkers in multiple myeloma using meta-analysis of mRNA and miRNA expression data

Multiple myeloma (MM) is a plasma cell malignancy with diverse clinical phenotypes and molecular heterogeneity not completely understood. Differentially expressed genes (DEGs) and miRNAs (DEMs) in MM may influence disease pathogenesis, clinical presentation / drug sensitivities. But these signatures overlap meagrely plausibly due to complexity of myeloma genome, diversity in primary cells studied, molecular technologies/ analytical tools utilized. This warrants further investigations since DEGs/DEMs can impact clinical outcomes and guide personalized therapy. We have conducted genome-wide meta-analysis of DEGs/DEMs in MM versus Normal Plasma Cells (NPCs) and derived unified putative signatures for MM. 100 DEMs and 1,362 DEGs were found deranged between MM and NPCs. Signatures of 37 DEMs ('Union 37') and 154 DEGs ('Union 154') were deduced that shared 17 DEMs and 22 DEGs with published prognostic signatures, respectively. Two miRs (miR-16-2-3p, 30d-2-3p) correlated with survival outcomes. PPI analysis identified 5 topmost functionally connected hub genes (UBC, ITGA4, HSP90AB1, VCAM1, VCP). Transcription factor regulatory networks were determined for five seed DEGs with ≥ 4 biomarker applications (CDKN1A, CDKN2A, MMP9, IGF1, MKI67) and three topmost up/ down regulated DEMs (miR-23b, 195, let7b/ miR-20a, 155, 92a). Further studies are warranted to establish and translate prognostic potential of these signatures for MM.

Promoting effects of MiR-135b on human multiple myeloma cells via regulation of the Wnt/β-catenin/Versican signaling pathway

MicroRNA (MiR)-135b and its mediated Wnt/β-catenin signaling pathway are involved in human malignancies. However, their roles in multiple myeloma (MM) remained poorly understood. Our study aimed to uncover their roles in MM. MiR-135b and Versican expressions were measured using quantitative real-time polymerase chain reaction (qRT-PCR). MM cell proliferation, apoptosis, migration and invasion were detected by cell counting kit-8 (CCK-8) assay, flow cytometry, wound healing assay and transwell assay, respectively. Relative expression of Wnt/β-catenin signaling pathway-related protein was quantified by Western blot. MiR-135b was upregulated in the serum of MM patients, and miR-135b upregulation promoted MM cell proliferation, migration and invasion but suppressed apoptosis. Also, miR-135b upregulation promoted activation of Wnt/β-catenin signaling pathway. However, downregulation of miR-135b caused an opposite effect. After incubating cells with miR-135b inhibitor and Wnt/β-catenin signaling pathway agonist Lithium chloride (LiCl), which reversed the effects of downregulating miR-135b. Versican is the downstream effector of the Wnt/β-catenin signaling pathway, and its silencing reversed the effects of LiCl on MM cells. In conclusion, miR-135b and its mediated Wnt/β-catenin signaling pathway promoted proliferation, migration and invasion but suppressed apoptosis of MM cells through regulating Versican, providing a possible treatment for MM.

Identification of G2 and S Phase-Expressed-1 as a Potential Biomarker in Patients with Prostate Cancer

Background

This study aimed to predict and explore the possible clinical value and mechanism of genetic markers in prostate cancer (PCa) using a bioinformatics analysis method.

Materials and Methods

The RNA-seq data were downloaded from The Cancer Genome Atlas (TCGA) database to identify the differentially expressed genes (DEGs). The hub genes were screened by building protein–protein interaction (PPI) subnetworks with four topological analysis methods. The overall survival analysis of hub genes was conducted using Kaplan–Meier curves. Furthermore, the bioinformatics results were confirmed in 102 PCa samples collected in our hospital. Gene Set Enrichment Analysis (GSEA) was performed to provide information about the molecular mechanisms underlying PCa.

Results

Among 13 hub genes, the high expression of GTSE1 or KIF18B was associated with worse overall survival according to the TCGA samples. Immunoreactive scores for GTSE1 staining were significantly higher in PCa tissues than in paracancerous tissues (P<0.01). The overall survival time of patients with high GTSE1 expression was shorter than that of patients with low GTSE1 expression (P=0.015). GSEA demonstrated that high GTSE1 expression was mainly enriched in the cell cycle (P<0.001), DNA replication (P<0.001), mismatch repair (P<0.001), and p53 signaling pathway (P<0.001).

Conclusion

GTSE1 expression was significantly high in PCa and associated with poor prognosis. GTSE1 may serve as a potential biomarker and therapeutic target in PCa patients.

Identification of cancer/testis antigen 2 gene as a potential hepatocellular carcinoma therapeutic target by hub gene screening with topological analysis

The 5-year survival rate of hepatocellular carcinoma (HCC) is <20%; thus, identifying new potential therapeutic targets or novel biomarkers for prognosis prediction is crucial. The present study aimed to screen hub genes by constructing protein-protein interaction (PPI) subnetworks using topological analysis methods, as well as reveal their clinical significance through big data analytics and their association with the clinicopathological features. Firstly, the PPI subnetworks were constructed using four topological analysis methods, including the MCC, DMNC, MNC and degree methods, to obtain 6 hub genes. Subsequently, the hub gene cancer/testis antigen 2 (CTAG2), which affects the prognosis of HCC (overall survival, P=0.035), was acquired by analysing clinical data in The Cancer Genome Atlas database. Meanwhile, CTAG2 expression was significantly associated with the age at diagnosis (P=0.003), T stage (P=0.028), TNM stage (P=0.028) and α-fetoprotein (AFP) expression (P=0.045). Further immunohistochemical analysis of samples collected in our hospital revealed that the expression level of CTAG2 in 46 HCC tissues was significantly higher in comparison with that in paired adjacent tissues. The clinical data indicated that the expression of CTAG2 was significantly correlated with the hepatitis B virus status (P=0.010) and AFP expression (P=0.004). These results were then found to be consistent with the results of big data analytics. Furthermore, Gene Set Enrichment Analysis demonstrated that the function of CTAG2 in HCC may be associated with the cell cycle. Taken together, these findings suggest that CTAG2 may serve as a new potential therapeutic target for HCC patients.

Comprehensive analysis of key genes and microRNAs in radioresistant nasopharyngeal carcinoma

Background

Radioresistance is one of the main obstacle limiting the therapeutic efficacy and prognosis of patients, the molecular mechanisms of radioresistance is still unclear. The purpose of this study was to identify the key genes and miRNAs and to explore their potential molecular mechanisms in radioresistant nasopharyngeal carcinoma.

Methods

In this study, we analysis the differentially expressed genes and microRNA based on the database of GSE48501 and GSE48502, and then employed bioinformatics to analyze the pathways and GO terms in which DEGs and DEMS target genes are involved. Moreover, Construction of protein-protein interaction network and identification of hub genes. Finally, analyzed the biological networks for validated target gene of hub miRNAs.

Results

A total of 373 differentially expressed genes (DEGs) and 14 differentially expressed microRNAs (DEMs) were screened out. The up-regulated gene JUN was overlap both in DEGs and publicly available studies, which was potentially targeted by three miRNAs, including hsa-miR-203, hsa-miR-24 and hsa-miR-31. Moreover, Pathway analysis showed that both up-regulated gene and DEMs target genes were enriched in TGF-beta signaling pathway, Hepatitis B, Pathways in cancer and p53 signaling pathway. Finally, we further constructed protein-protein interaction network (PPI) of DEGs and analyzed the biological networks for above mentioned common miRNAs, the result indicated that JUN was a core hub gene in PPI network, hsa-miR-24 and its target gene were significantly enriched in P53 signaling pathway.

Conclusions

These results might provide new clues to improve the radiosensitivity of Nasopharyngeal Carcinoma.

Electronic supplementary material

The online version of this article (10.1186/s12920-019-0507-6) contains supplementary material, which is available to authorized users.

Identification of Key Candidate Genes and Pathways in Endometrial Cancer by Integrated Bioinformatical Analysis

Endometrial Cancer is the most common female genital tract malignancy, its pathogenesis is complex, not yet fully described. To identify key genes of Endometrial Cancer we downloaded the gene chip GSE17025 from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified through the GEO2R analysis tool. Functional and pathway enrichment analysis were performed for DEGs using DAVID database. The network of protein–protein-interaction (PPI) was established by STRING website and visualized by Cytoscape. Then, functional and pathway enrichment analysis of DEGS were performed by DAVID database. A total of 1000 significant differences genes were obtained, contain 362 up-regulated genes and 638 down-regulated genes. PCDH10, SLC6A2, OGN, SFRP4, TRH, ANGPTL, FOSB are down-regulated genes. The gene of IGH, CCL20, ELF5, LTF, ASPM expression level in tumor patients are up-regulated. Biological function of enrichment include metabolism of xenobiotics by cytochrome P450, MAPK signaling pathway, Serotonergic synapse, Protein digestion and absorption, IL-17 signaling pathway, Chemokine signaling pathway, HIF-1 signaling pathway, p53 signaling pathway. All in all, the current study to determine endometrial differentially expressed genes and biological function, comprehensive analysis of intrauterine membrane carcinoma pathogenesis mechanism, and might be used as molecular targets and diagnostic biomarkers for the treatment of endometrial cancer.

Long non‑coding RNA HOTTIP promotes hepatocellular carcinoma tumorigenesis and development: A comprehensive investigation based on bioinformatics, qRT‑PCR and meta‑analysis of 393 cases

HOTTIP functions as an independent biomarker in multiple cancers. However, the role of HOTTIP in hepatocellular carcinoma (HCC) remains unclear. In this study, we sought to investigate the HOTTIP expression in HCC and normal liver. We combined quantitative reverse transcription-polymerase chain reactions (qRT-PCR), Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), Multi Experiment Matrix (MEM) and Oncomine database to assess the clinical role and the potential molecular mechanism of HOTTIP in HCC. Furthermore, a meta-analysis was performed to evaluate the relationship between HOTTIP and HCC tumorigenesis and development. Additionally, bioinformatics analysis, which contained Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and network analysis, were applied to investigate the underlying functions, pathways and networks of the potential genes. HOTTIP was obviously upregulated in HCC. A statistically significant higher expression of HOTTIP was found in TNM (III +IV), age (≥60), sex (male), race (white) and cirrhosis (no) compared to the control groups (P<0.05). Furthermore, the meta-analysis of 393 cases from multiple centers indicated that HOTTIP had high diagnostic value in HCC. Additionally, according to GO and KEGG analyses, we found that the most strongly enriched functional terms were gland development, transcription factor activity and extrinsic to membrane. Also, the HOTTIP co-expressed genes were significantly related to PPAR signaling pathway. We speculate that HOTTIP might play a vital part in HCC via regulating various pathways, especially PPAR signaling pathway. However, the detailed mechanism should be confirmed by functional experiments.